README.md

metadata

language:
  - multilingual
license: apache-2.0

Model Card for Sindibad-7B

Table of Contents

0. TL;DR
1. Model Details
2. Usage
3. Training Details
4. Evaluation

TL;DR

Model Details

Model Description

• Model type: Language model
• Language(s) (NLP): English
• License: Apache 2.0

Usage

Find below some example scripts on how to use the model in transformers (Make sure to have the latest transformers, or the one built from source):

Using the Pytorch model

Running the model on a CPU

Click to expand

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b")

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Running the model on a GPU

Click to expand

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b", device_map="auto")

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Running the model on a GPU using different precisions

FP16

Click to expand

# pip install accelerate
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b", device_map="auto", torch_dtype=torch.float16)

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

INT8

Click to expand

# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b", device_map="auto", load_in_8bit=True)

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Training Details

Training Data

Jingwei

Training Procedure

Maksim

Evaluation

Results

Ilyas